{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepallength  sepalwidth  petallength  petalwidth\n",
      "0            5.1         3.5          1.4         0.2\n",
      "1            4.9         3.0          1.4         0.2\n",
      "2            4.7         3.2          1.3         0.2\n",
      "3            4.6         3.1          1.5         0.2\n",
      "4            5.0         3.6          1.4         0.2\n",
      "..           ...         ...          ...         ...\n",
      "145          6.7         3.0          5.2         2.3\n",
      "146          6.3         2.5          5.0         1.9\n",
      "147          6.5         3.0          5.2         2.0\n",
      "148          6.2         3.4          5.4         2.3\n",
      "149          5.9         3.0          5.1         1.8\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(r\"dataset/iris.arff.csv\", header=0)\n",
    "data.drop([\"class\"], axis=1, inplace=True)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KNN:\n",
    "    def __init__(self, k):\n",
    "        self.k = k\n",
    "    def fit(self, X, y):\n",
    "        '''训练\n",
    "        \n",
    "        Parameeters\n",
    "        -----\n",
    "        X: 类数组类型，可以是List也可以是Ndarray，形状为： [样本数量,特征数量]\n",
    "        y: 类数组类型，形状为：[样本数量]\n",
    "        \n",
    "        '''\n",
    "        self.X = np.asarray(X)\n",
    "        self.y = np.asarray(y)\n",
    "    def predict(self, X):\n",
    "        '''对样本进行预测\n",
    "        Parameters:\n",
    "        X: 类数组类型，可以是List也可以是Ndarray，形状为： [样本数量,特征数量]\n",
    "        Returns:\n",
    "        数组类型，预测结果\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        result = []\n",
    "        for x in X:\n",
    "            dis = np.sqrt(np.sum((x - self.X)**2, axis=1))\n",
    "            index = dis.argsort()\n",
    "            index = index[:self.k]\n",
    "            result.append(np.mean(self.y[index])) # 计算均值\n",
    "        return np.asarray(result)\n",
    "    def predict2(self, X):\n",
    "        '''对样本进行预测，考虑权重\n",
    "        \n",
    "        权重计算方式：使用每个节点(邻居)距离的倒数/所有节点距离倒数只和\n",
    "        \n",
    "        Parameters:\n",
    "        X: 类数组类型，可以是List也可以是Ndarray，形状为： [样本数量,特征数量]\n",
    "        Returns:\n",
    "        数组类型，预测结果\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        result = []\n",
    "        for x in X:\n",
    "            # 计算与训练集的距离，取平方后开方\n",
    "            dis = np.sqrt(np.sum((x - self.X)**2, axis=1))\n",
    "            index = dis.argsort()\n",
    "            index = index[:self.k]\n",
    "            s = np.sum(1/(dis[index]+0.001)) # 最后加一个一很小的数就是为了避免距离为0的情况\n",
    "            weight = (1/(dis[index]+0.001))/s # 距离倒数/倒数之和\n",
    "            result.append(np.sum(self.y[index]*weight)) # 邻居节点标签纸*对应权重相加求和\n",
    "        return np.asarray(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.33333333, 2.06666667, 0.2       , 0.2       , 1.9       ,\n",
       "       0.23333333, 2.2       , 1.26666667, 1.2       , 1.16666667,\n",
       "       1.93333333, 2.13333333, 1.83333333, 1.93333333, 0.13333333,\n",
       "       1.16666667, 2.23333333, 1.96666667, 0.3       , 1.46666667,\n",
       "       1.26666667, 1.66666667, 1.33333333, 0.26666667, 0.2       ,\n",
       "       0.13333333, 2.03333333, 1.26666667, 2.2       , 0.23333333])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.2, 2. , 0.3, 0.2, 1.9, 0.2, 2.4, 1.3, 1.2, 1. , 2.3, 2.3, 1.5,\n",
       "       1.7, 0.2, 1. , 2.4, 1.9, 0.2, 1.3, 1.3, 1.8, 1.3, 0.2, 0.4, 0.1,\n",
       "       1.8, 1. , 2.2, 0.2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = data.sample(len(data), random_state=0)\n",
    "train_X = t.iloc[:120, :-1]\n",
    "train_y = t.iloc[:120, -1]\n",
    "test_X = t.iloc[120:, :-1]\n",
    "test_y = t.iloc[120:, -1]\n",
    "knn = KNN(k=3)\n",
    "knn.fit(train_X, train_y)\n",
    "result = knn.predict(test_X)\n",
    "result2 = knn.predict2(test_X)\n",
    "display(result)\n",
    "display(test_y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7244444444444444"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7650802020943153"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(np.mean(np.sum((result - test_y)**2)))\n",
    "display(np.mean(np.sum((result2 - test_y)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rcParams[\"font.family\"] = \"SimHei\"\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False # 显示负号\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(result, 'ro-')  # 红色圆圈表示\"不带\"权重鱼的预测值\n",
    "plt.plot(result2, 'bo-') # 蓝色圆圈表示\"带\"权重鱼的预测值\n",
    "plt.plot(test_y.values, 'go-') # 绿色圆圈表示测试集真实值\n",
    "plt.title('KNN连续预测展示')\n",
    "plt.xlabel('节点序号')\n",
    "plt.ylabel('花瓣长度')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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